Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = '/data'  # Changed for Udacity Workspaces from './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f571a83e320>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f571a760f28>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.3.0
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # Implement Function
    inputs_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    inputs_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32, (None), name='learning_rate')

    return inputs_real, inputs_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/opt/conda/lib/python3.6/runpy.py", line 193, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/opt/conda/lib/python3.6/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 497, in start\n    self.io_loop.start()', 'File "/opt/conda/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 450, in _handle_events\n    self._handle_recv()', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 480, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 432, in _run_callback\n    callback(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 233, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 399, in execute_request\n    user_expressions, allow_stdin)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 208, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/opt/conda/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 537, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2662, in run_cell\n    raw_cell, store_history, silent, shell_futures)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2785, in _run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2907, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2961, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-5-bd8a413aefe7>", line 23, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/home/workspace/gan-face-generation/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/home/workspace/gan-face-generation/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/home/workspace/gan-face-generation/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/home/workspace/gan-face-generation/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 175, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 144, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 101, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [15]:
def discriminator(images, reuse=False, alpha=0.2):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        # Input layer from celeba is 28x28x3
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        lrelu1 = tf.maximum(alpha * x1, x1)
        # now 14x14x64
        x2 = tf.layers.conv2d(lrelu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        lrelu2 = tf.maximum(alpha * bn2, bn2)
        # now 7x7x128
        x3 = tf.layers.conv2d(lrelu2, 265, 5, strides=1, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        lrelu3 = tf.maximum(alpha * bn3, bn3)
        # now 7x7x265
        x4 = tf.layers.conv2d(lrelu3, 512, 5, strides=1, padding='same')
        bn4 = tf.layers.batch_normalization(x4, training=True)
        lrelu4 = tf.maximum(alpha * bn4, bn4)
        # now 7x7x512
                
        flat = tf.reshape(lrelu4, (-1, 7*7*512))
        
        logits = tf.layers.dense(flat, 1)
        out = tf.nn.sigmoid(logits)

        return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [16]:
def generator(z, out_channel_dim, is_train=True, alpha=0.2):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    reuse = True
    if(is_train):
        reuse=False
    
    # Implement Function
    with tf.variable_scope('generator', reuse=reuse):
        # Fully connected layer
        x1 = tf.layers.dense(z, 7*7*512)
        x1 = tf.reshape(x1, (-1, 7, 7, 512))
        bn1 = tf.layers.batch_normalization(x1, training=is_train)
        lrelu1 = tf.maximum(alpha * bn1, bn1)
        # now 7x7x512
        
        # Convolutional layers
        x2 = tf.layers.conv2d_transpose(lrelu1, 256, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=is_train)
        lrelu2 = tf.maximum(alpha * bn2, bn2)
        #now 14x14x265
        
        x3 = tf.layers.conv2d_transpose(lrelu2, 128, 5, strides=2, padding='same')
        bn3 = tf.layers.batch_normalization(x3, training=is_train)
        lrelu3 = tf.maximum(alpha * bn3, bn3)
        #now 28x28x128
        
        x4 = tf.layers.conv2d_transpose(lrelu3, 64, 5, strides=1, padding='same')
        bn4 = tf.layers.batch_normalization(x4, training=is_train)
        lrelu4 = tf.maximum(alpha * bn4, bn4)
        #now 28x28x64
        
        logits = tf.layers.conv2d_transpose(lrelu4, out_channel_dim, 5, strides=1, padding='same')
        # 28x28xout_channel_dim
        out = tf.nn.tanh(logits)
        
        return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [35]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # For label smoothing for the discriminator at d_loss_real
    smooth = 0.1
    
    # Implement Function
    g_model = generator(input_z, out_channel_dim)
    d_model_real, d_logits_real = discriminator(input_real)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)
    
    d_loss_real = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=(tf.ones_like(d_model_real)  * (1 - smooth))))
    d_loss_fake = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    
    g_loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels = tf.ones_like(d_model_fake)))
    
    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [18]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # Implement Function
    t_vars = tf.trainable_variables()
    
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    
    # From dcgan-svhn
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
    
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [19]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [36]:
# Adapted from https://github.com/udacity/deep-learning-udacity-material/blob/master/dcgan-svhn/DCGAN_Exercises.ipynb
def scale(x):
    # min = -0.5 and max = 0.5. To scale it to [-1,1] need to multiply by 2
    x = x*2.0
    return x
In [37]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """    
    # Build Model
    sample_z = np.random.uniform(-1, 1, size=(72, z_dim)) # For sampling
    steps = 0
    
    # print(data_shape) with MNIST: (60000, 28, 28, 1)
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_opt, g_opt = model_opt(d_loss, g_loss, lr, beta1)
        
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            for batch_images in get_batches(batch_size):
                batch_images = scale(batch_images)
                steps += 1
                #print(batch_images.shape) # MNIST: 128, 28, 28, 1
                #print(batch_images.min())  # -0.5
                #print(batch_images.max())  # 0.5
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim)) # Get random noise
                
                # Optimizers
                _ = sess.run(d_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                
                # Printing losses - from dcgan-svhn
                if steps % 50 == 0:
                    train_loss_d = d_loss.eval({input_z: batch_z, input_real: batch_images, lr: learning_rate})
                    train_loss_g = g_loss.eval({input_z: batch_z, lr: learning_rate})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Steps {}".format(steps),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))
                    
                    # Showing examples
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)    

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [38]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Steps 50 Discriminator Loss: 7.3008... Generator Loss: 8.8086
Epoch 1/2... Steps 100 Discriminator Loss: 1.8409... Generator Loss: 0.6017
Epoch 1/2... Steps 150 Discriminator Loss: 1.3757... Generator Loss: 0.8626
Epoch 1/2... Steps 200 Discriminator Loss: 1.2544... Generator Loss: 1.0156
Epoch 1/2... Steps 250 Discriminator Loss: 2.4388... Generator Loss: 0.1564
Epoch 1/2... Steps 300 Discriminator Loss: 1.3866... Generator Loss: 1.7508
Epoch 1/2... Steps 350 Discriminator Loss: 1.4510... Generator Loss: 0.7976
Epoch 1/2... Steps 400 Discriminator Loss: 1.5895... Generator Loss: 0.4666
Epoch 1/2... Steps 450 Discriminator Loss: 1.6398... Generator Loss: 0.5052
Epoch 1/2... Steps 500 Discriminator Loss: 1.8007... Generator Loss: 2.0098
Epoch 1/2... Steps 550 Discriminator Loss: 1.3932... Generator Loss: 0.7716
Epoch 1/2... Steps 600 Discriminator Loss: 1.4860... Generator Loss: 0.5108
Epoch 1/2... Steps 650 Discriminator Loss: 1.7583... Generator Loss: 0.3549
Epoch 1/2... Steps 700 Discriminator Loss: 1.2263... Generator Loss: 1.5313
Epoch 1/2... Steps 750 Discriminator Loss: 2.1343... Generator Loss: 0.2579
Epoch 1/2... Steps 800 Discriminator Loss: 2.2611... Generator Loss: 3.0516
Epoch 1/2... Steps 850 Discriminator Loss: 1.2022... Generator Loss: 0.7607
Epoch 1/2... Steps 900 Discriminator Loss: 0.9707... Generator Loss: 1.8395
Epoch 1/2... Steps 950 Discriminator Loss: 1.3018... Generator Loss: 1.0158
Epoch 1/2... Steps 1000 Discriminator Loss: 1.5274... Generator Loss: 0.5015
Epoch 1/2... Steps 1050 Discriminator Loss: 1.1491... Generator Loss: 0.9127
Epoch 1/2... Steps 1100 Discriminator Loss: 1.1631... Generator Loss: 1.5404
Epoch 1/2... Steps 1150 Discriminator Loss: 1.2352... Generator Loss: 1.4571
Epoch 1/2... Steps 1200 Discriminator Loss: 0.9252... Generator Loss: 1.3723
Epoch 1/2... Steps 1250 Discriminator Loss: 0.9279... Generator Loss: 2.1376
Epoch 1/2... Steps 1300 Discriminator Loss: 0.7739... Generator Loss: 2.1169
Epoch 1/2... Steps 1350 Discriminator Loss: 1.8327... Generator Loss: 0.4824
Epoch 1/2... Steps 1400 Discriminator Loss: 1.1724... Generator Loss: 1.7912
Epoch 1/2... Steps 1450 Discriminator Loss: 1.0556... Generator Loss: 1.5637
Epoch 1/2... Steps 1500 Discriminator Loss: 1.0529... Generator Loss: 0.8267
Epoch 1/2... Steps 1550 Discriminator Loss: 0.8688... Generator Loss: 1.4136
Epoch 1/2... Steps 1600 Discriminator Loss: 1.0312... Generator Loss: 1.7480
Epoch 1/2... Steps 1650 Discriminator Loss: 1.6167... Generator Loss: 0.4836
Epoch 1/2... Steps 1700 Discriminator Loss: 0.9306... Generator Loss: 1.5994
Epoch 1/2... Steps 1750 Discriminator Loss: 0.5978... Generator Loss: 2.2914
Epoch 1/2... Steps 1800 Discriminator Loss: 0.8894... Generator Loss: 1.1465
Epoch 1/2... Steps 1850 Discriminator Loss: 1.2246... Generator Loss: 1.4626
Epoch 2/2... Steps 1900 Discriminator Loss: 1.1305... Generator Loss: 0.7906
Epoch 2/2... Steps 1950 Discriminator Loss: 1.1581... Generator Loss: 1.0596
Epoch 2/2... Steps 2000 Discriminator Loss: 0.7348... Generator Loss: 1.8137
Epoch 2/2... Steps 2050 Discriminator Loss: 2.7054... Generator Loss: 0.1677
Epoch 2/2... Steps 2100 Discriminator Loss: 1.1088... Generator Loss: 1.2046
Epoch 2/2... Steps 2150 Discriminator Loss: 0.9050... Generator Loss: 1.3584
Epoch 2/2... Steps 2200 Discriminator Loss: 1.6191... Generator Loss: 0.5875
Epoch 2/2... Steps 2250 Discriminator Loss: 0.7048... Generator Loss: 1.4790
Epoch 2/2... Steps 2300 Discriminator Loss: 0.9280... Generator Loss: 1.0638
Epoch 2/2... Steps 2350 Discriminator Loss: 0.9448... Generator Loss: 2.0528
Epoch 2/2... Steps 2400 Discriminator Loss: 1.5998... Generator Loss: 0.5189
Epoch 2/2... Steps 2450 Discriminator Loss: 0.9293... Generator Loss: 2.1831
Epoch 2/2... Steps 2500 Discriminator Loss: 0.7472... Generator Loss: 1.7280
Epoch 2/2... Steps 2550 Discriminator Loss: 1.3694... Generator Loss: 0.7670
Epoch 2/2... Steps 2600 Discriminator Loss: 0.9095... Generator Loss: 1.0923
Epoch 2/2... Steps 2650 Discriminator Loss: 2.3407... Generator Loss: 0.2636
Epoch 2/2... Steps 2700 Discriminator Loss: 2.6952... Generator Loss: 0.2720
Epoch 2/2... Steps 2750 Discriminator Loss: 0.8812... Generator Loss: 1.0645
Epoch 2/2... Steps 2800 Discriminator Loss: 1.7130... Generator Loss: 0.4445
Epoch 2/2... Steps 2850 Discriminator Loss: 1.3114... Generator Loss: 0.7971
Epoch 2/2... Steps 2900 Discriminator Loss: 1.2067... Generator Loss: 0.7316
Epoch 2/2... Steps 2950 Discriminator Loss: 0.9806... Generator Loss: 1.4964
Epoch 2/2... Steps 3000 Discriminator Loss: 1.2182... Generator Loss: 0.8849
Epoch 2/2... Steps 3050 Discriminator Loss: 0.7638... Generator Loss: 1.7321
Epoch 2/2... Steps 3100 Discriminator Loss: 0.8628... Generator Loss: 1.7869
Epoch 2/2... Steps 3150 Discriminator Loss: 0.8209... Generator Loss: 1.1578
Epoch 2/2... Steps 3200 Discriminator Loss: 0.5509... Generator Loss: 2.1694
Epoch 2/2... Steps 3250 Discriminator Loss: 2.2846... Generator Loss: 5.2048
Epoch 2/2... Steps 3300 Discriminator Loss: 0.7881... Generator Loss: 2.1542
Epoch 2/2... Steps 3350 Discriminator Loss: 1.2646... Generator Loss: 1.1865
Epoch 2/2... Steps 3400 Discriminator Loss: 0.9570... Generator Loss: 2.1876
Epoch 2/2... Steps 3450 Discriminator Loss: 0.8458... Generator Loss: 1.2120
Epoch 2/2... Steps 3500 Discriminator Loss: 1.6162... Generator Loss: 0.4833
Epoch 2/2... Steps 3550 Discriminator Loss: 0.5790... Generator Loss: 2.0317
Epoch 2/2... Steps 3600 Discriminator Loss: 0.7635... Generator Loss: 1.2949
Epoch 2/2... Steps 3650 Discriminator Loss: 1.4370... Generator Loss: 0.6085
Epoch 2/2... Steps 3700 Discriminator Loss: 1.3943... Generator Loss: 0.5678
Epoch 2/2... Steps 3750 Discriminator Loss: 1.6033... Generator Loss: 0.5277

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [ ]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.2


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/2... Steps 50 Discriminator Loss: 2.0208... Generator Loss: 0.6346
Epoch 1/2... Steps 100 Discriminator Loss: 1.5832... Generator Loss: 2.2973
Epoch 1/2... Steps 150 Discriminator Loss: 1.8946... Generator Loss: 0.3634
Epoch 1/2... Steps 200 Discriminator Loss: 0.7221... Generator Loss: 1.7701
Epoch 1/2... Steps 250 Discriminator Loss: 2.0733... Generator Loss: 0.2358
Epoch 1/2... Steps 300 Discriminator Loss: 1.2323... Generator Loss: 1.2084
Epoch 1/2... Steps 350 Discriminator Loss: 1.9284... Generator Loss: 0.2597
Epoch 1/2... Steps 400 Discriminator Loss: 2.4707... Generator Loss: 0.1373
Epoch 1/2... Steps 450 Discriminator Loss: 1.3128... Generator Loss: 0.5584
Epoch 1/2... Steps 500 Discriminator Loss: 0.7802... Generator Loss: 1.7831
Epoch 1/2... Steps 550 Discriminator Loss: 1.6947... Generator Loss: 2.8953
Epoch 1/2... Steps 600 Discriminator Loss: 1.5201... Generator Loss: 1.4157
Epoch 1/2... Steps 650 Discriminator Loss: 0.9945... Generator Loss: 2.0629
Epoch 1/2... Steps 700 Discriminator Loss: 1.3377... Generator Loss: 1.8916
Epoch 1/2... Steps 750 Discriminator Loss: 1.0339... Generator Loss: 0.8803
Epoch 1/2... Steps 800 Discriminator Loss: 1.4189... Generator Loss: 1.7628
Epoch 1/2... Steps 850 Discriminator Loss: 1.2183... Generator Loss: 1.3936
Epoch 1/2... Steps 900 Discriminator Loss: 1.3796... Generator Loss: 1.0058
Epoch 1/2... Steps 950 Discriminator Loss: 1.3782... Generator Loss: 0.9999
Epoch 1/2... Steps 1000 Discriminator Loss: 0.8124... Generator Loss: 1.1052
Epoch 1/2... Steps 1050 Discriminator Loss: 0.6871... Generator Loss: 1.8976
Epoch 1/2... Steps 1100 Discriminator Loss: 1.0202... Generator Loss: 0.8768
Epoch 1/2... Steps 1150 Discriminator Loss: 1.0685... Generator Loss: 1.0948
Epoch 1/2... Steps 1200 Discriminator Loss: 0.7773... Generator Loss: 1.2630
Epoch 1/2... Steps 1250 Discriminator Loss: 1.1086... Generator Loss: 1.9243
Epoch 1/2... Steps 1300 Discriminator Loss: 0.8603... Generator Loss: 4.0936
Epoch 1/2... Steps 1350 Discriminator Loss: 0.8151... Generator Loss: 2.0114
Epoch 1/2... Steps 1400 Discriminator Loss: 2.7511... Generator Loss: 2.8561
Epoch 1/2... Steps 1450 Discriminator Loss: 1.3728... Generator Loss: 0.8924
Epoch 1/2... Steps 1500 Discriminator Loss: 1.3190... Generator Loss: 0.7953
Epoch 1/2... Steps 1550 Discriminator Loss: 1.2982... Generator Loss: 0.7366
Epoch 1/2... Steps 1600 Discriminator Loss: 1.1794... Generator Loss: 0.8777
Epoch 1/2... Steps 1650 Discriminator Loss: 0.7406... Generator Loss: 1.7014
Epoch 1/2... Steps 1700 Discriminator Loss: 1.3599... Generator Loss: 0.9776
Epoch 1/2... Steps 1750 Discriminator Loss: 0.9792... Generator Loss: 0.8478
Epoch 1/2... Steps 1800 Discriminator Loss: 0.7927... Generator Loss: 2.3858
Epoch 1/2... Steps 1850 Discriminator Loss: 1.3428... Generator Loss: 1.3048
Epoch 1/2... Steps 1900 Discriminator Loss: 0.7929... Generator Loss: 1.2817
Epoch 1/2... Steps 1950 Discriminator Loss: 0.7351... Generator Loss: 2.8566
Epoch 1/2... Steps 2000 Discriminator Loss: 1.5501... Generator Loss: 0.3822
Epoch 1/2... Steps 2050 Discriminator Loss: 1.3098... Generator Loss: 0.6725
Epoch 1/2... Steps 2100 Discriminator Loss: 1.3534... Generator Loss: 1.9791
Epoch 1/2... Steps 2150 Discriminator Loss: 1.2537... Generator Loss: 1.8088
Epoch 1/2... Steps 2200 Discriminator Loss: 1.5603... Generator Loss: 0.5481
Epoch 1/2... Steps 2250 Discriminator Loss: 1.5169... Generator Loss: 2.3559
Epoch 1/2... Steps 2300 Discriminator Loss: 1.3476... Generator Loss: 1.3490
Epoch 1/2... Steps 2350 Discriminator Loss: 1.2489... Generator Loss: 0.5940
Epoch 1/2... Steps 2400 Discriminator Loss: 0.9319... Generator Loss: 1.7587
Epoch 1/2... Steps 2450 Discriminator Loss: 0.5463... Generator Loss: 2.1668
Epoch 1/2... Steps 2500 Discriminator Loss: 1.2104... Generator Loss: 0.9276
Epoch 1/2... Steps 2550 Discriminator Loss: 1.2346... Generator Loss: 1.4989
Epoch 1/2... Steps 2600 Discriminator Loss: 1.0567... Generator Loss: 0.9091
Epoch 1/2... Steps 2650 Discriminator Loss: 1.9388... Generator Loss: 2.8842
Epoch 1/2... Steps 2700 Discriminator Loss: 2.4959... Generator Loss: 2.9468
Epoch 1/2... Steps 2750 Discriminator Loss: 1.4124... Generator Loss: 1.7700
Epoch 1/2... Steps 2800 Discriminator Loss: 0.8874... Generator Loss: 1.6515
Epoch 1/2... Steps 2850 Discriminator Loss: 1.3916... Generator Loss: 0.5645
Epoch 1/2... Steps 2900 Discriminator Loss: 1.3780... Generator Loss: 3.5773
Epoch 1/2... Steps 2950 Discriminator Loss: 1.0925... Generator Loss: 1.0881
Epoch 1/2... Steps 3000 Discriminator Loss: 1.2964... Generator Loss: 0.5444
Epoch 1/2... Steps 3050 Discriminator Loss: 0.9033... Generator Loss: 2.2166
Epoch 1/2... Steps 3100 Discriminator Loss: 0.8539... Generator Loss: 1.5812
Epoch 1/2... Steps 3150 Discriminator Loss: 1.2627... Generator Loss: 1.2013
Epoch 1/2... Steps 3200 Discriminator Loss: 0.7648... Generator Loss: 1.6043
Epoch 1/2... Steps 3250 Discriminator Loss: 1.7489... Generator Loss: 0.3737
Epoch 1/2... Steps 3300 Discriminator Loss: 1.3514... Generator Loss: 1.8083
Epoch 1/2... Steps 3350 Discriminator Loss: 1.7229... Generator Loss: 0.3758
Epoch 1/2... Steps 3400 Discriminator Loss: 1.1474... Generator Loss: 1.3455
Epoch 1/2... Steps 3450 Discriminator Loss: 0.5917... Generator Loss: 2.0112
Epoch 1/2... Steps 3500 Discriminator Loss: 1.5202... Generator Loss: 2.0720
Epoch 1/2... Steps 3550 Discriminator Loss: 1.1856... Generator Loss: 0.7103
Epoch 1/2... Steps 3600 Discriminator Loss: 0.5784... Generator Loss: 3.3521
Epoch 1/2... Steps 3650 Discriminator Loss: 2.0046... Generator Loss: 3.1886
Epoch 1/2... Steps 3700 Discriminator Loss: 1.1846... Generator Loss: 1.2864
Epoch 1/2... Steps 3750 Discriminator Loss: 1.4683... Generator Loss: 0.6199
Epoch 1/2... Steps 3800 Discriminator Loss: 1.3252... Generator Loss: 0.6444
Epoch 1/2... Steps 3850 Discriminator Loss: 1.8443... Generator Loss: 1.8582
Epoch 1/2... Steps 3900 Discriminator Loss: 1.3223... Generator Loss: 0.7778
Epoch 1/2... Steps 3950 Discriminator Loss: 1.2900... Generator Loss: 0.6305
Epoch 1/2... Steps 4000 Discriminator Loss: 1.3378... Generator Loss: 2.8755
Epoch 1/2... Steps 4050 Discriminator Loss: 1.2733... Generator Loss: 1.4208
Epoch 1/2... Steps 4100 Discriminator Loss: 1.5315... Generator Loss: 0.4898
Epoch 1/2... Steps 4150 Discriminator Loss: 1.1881... Generator Loss: 0.6704
Epoch 1/2... Steps 4200 Discriminator Loss: 2.1740... Generator Loss: 0.2059
Epoch 1/2... Steps 4250 Discriminator Loss: 1.3784... Generator Loss: 0.4949
Epoch 1/2... Steps 4300 Discriminator Loss: 0.9443... Generator Loss: 1.7084
Epoch 1/2... Steps 4350 Discriminator Loss: 0.8981... Generator Loss: 1.1253
Epoch 1/2... Steps 4400 Discriminator Loss: 0.6286... Generator Loss: 1.5556
Epoch 1/2... Steps 4450 Discriminator Loss: 1.3843... Generator Loss: 0.5695
Epoch 1/2... Steps 4500 Discriminator Loss: 1.4517... Generator Loss: 0.4521
Epoch 1/2... Steps 4550 Discriminator Loss: 0.9517... Generator Loss: 1.7298
Epoch 1/2... Steps 4600 Discriminator Loss: 2.4503... Generator Loss: 2.7879
Epoch 1/2... Steps 4650 Discriminator Loss: 1.1393... Generator Loss: 1.4884
Epoch 1/2... Steps 4700 Discriminator Loss: 1.7645... Generator Loss: 0.3153
Epoch 1/2... Steps 4750 Discriminator Loss: 1.7300... Generator Loss: 0.3369
Epoch 1/2... Steps 4800 Discriminator Loss: 0.8377... Generator Loss: 1.2349
Epoch 1/2... Steps 4850 Discriminator Loss: 1.4845... Generator Loss: 0.4541
Epoch 1/2... Steps 4900 Discriminator Loss: 1.1614... Generator Loss: 0.6415
Epoch 1/2... Steps 4950 Discriminator Loss: 1.1139... Generator Loss: 0.7978
Epoch 1/2... Steps 5000 Discriminator Loss: 1.0886... Generator Loss: 0.7619
Epoch 1/2... Steps 5050 Discriminator Loss: 0.7008... Generator Loss: 1.9114
Epoch 1/2... Steps 5100 Discriminator Loss: 1.2614... Generator Loss: 0.8156
Epoch 1/2... Steps 5150 Discriminator Loss: 1.1388... Generator Loss: 0.8763
Epoch 1/2... Steps 5200 Discriminator Loss: 1.3772... Generator Loss: 0.5802
Epoch 1/2... Steps 5250 Discriminator Loss: 0.9374... Generator Loss: 1.0153
Epoch 1/2... Steps 5300 Discriminator Loss: 0.7482... Generator Loss: 1.8470
Epoch 1/2... Steps 5350 Discriminator Loss: 0.9875... Generator Loss: 1.5961
Epoch 1/2... Steps 5400 Discriminator Loss: 0.8399... Generator Loss: 1.0987
Epoch 1/2... Steps 5450 Discriminator Loss: 1.5062... Generator Loss: 2.1608
Epoch 1/2... Steps 5500 Discriminator Loss: 1.1425... Generator Loss: 1.0112
Epoch 1/2... Steps 5550 Discriminator Loss: 0.8580... Generator Loss: 0.9971
Epoch 1/2... Steps 5600 Discriminator Loss: 1.0958... Generator Loss: 1.8929
Epoch 1/2... Steps 5650 Discriminator Loss: 0.9338... Generator Loss: 0.8716
Epoch 1/2... Steps 5700 Discriminator Loss: 1.1294... Generator Loss: 1.4143
Epoch 1/2... Steps 5750 Discriminator Loss: 1.0777... Generator Loss: 0.7876
Epoch 1/2... Steps 5800 Discriminator Loss: 0.7368... Generator Loss: 1.6201
Epoch 1/2... Steps 5850 Discriminator Loss: 1.0310... Generator Loss: 1.3286
Epoch 1/2... Steps 5900 Discriminator Loss: 0.5472... Generator Loss: 2.2486
Epoch 1/2... Steps 5950 Discriminator Loss: 1.1874... Generator Loss: 0.7977
Epoch 1/2... Steps 6000 Discriminator Loss: 1.0625... Generator Loss: 0.7530
Epoch 1/2... Steps 6050 Discriminator Loss: 1.1782... Generator Loss: 0.7643
Epoch 1/2... Steps 6100 Discriminator Loss: 0.9197... Generator Loss: 1.0631
Epoch 1/2... Steps 6150 Discriminator Loss: 1.5055... Generator Loss: 0.4409
Epoch 1/2... Steps 6200 Discriminator Loss: 1.1867... Generator Loss: 0.6443
Epoch 1/2... Steps 6250 Discriminator Loss: 0.9289... Generator Loss: 1.3511
Epoch 1/2... Steps 6300 Discriminator Loss: 0.8952... Generator Loss: 1.0535
Epoch 2/2... Steps 6350 Discriminator Loss: 1.5917... Generator Loss: 0.4211
Epoch 2/2... Steps 6400 Discriminator Loss: 0.8741... Generator Loss: 1.3737
Epoch 2/2... Steps 6450 Discriminator Loss: 1.1413... Generator Loss: 0.6500
Epoch 2/2... Steps 6500 Discriminator Loss: 0.8670... Generator Loss: 1.0898
Epoch 2/2... Steps 6550 Discriminator Loss: 1.1613... Generator Loss: 0.6504
Epoch 2/2... Steps 6600 Discriminator Loss: 1.1047... Generator Loss: 0.7750
Epoch 2/2... Steps 6650 Discriminator Loss: 1.0821... Generator Loss: 1.1369
Epoch 2/2... Steps 6700 Discriminator Loss: 1.2922... Generator Loss: 1.2128
Epoch 2/2... Steps 6750 Discriminator Loss: 1.0383... Generator Loss: 0.7768
Epoch 2/2... Steps 6800 Discriminator Loss: 1.7315... Generator Loss: 0.3576
Epoch 2/2... Steps 6850 Discriminator Loss: 1.0834... Generator Loss: 0.9478
Epoch 2/2... Steps 6900 Discriminator Loss: 1.1193... Generator Loss: 1.2804
Epoch 2/2... Steps 6950 Discriminator Loss: 1.2713... Generator Loss: 1.1037
Epoch 2/2... Steps 7000 Discriminator Loss: 1.3394... Generator Loss: 0.5275
Epoch 2/2... Steps 7050 Discriminator Loss: 1.2371... Generator Loss: 0.5867
Epoch 2/2... Steps 7100 Discriminator Loss: 1.0373... Generator Loss: 0.8068
Epoch 2/2... Steps 7150 Discriminator Loss: 1.5734... Generator Loss: 0.4263
Epoch 2/2... Steps 7200 Discriminator Loss: 1.9286... Generator Loss: 0.2851
Epoch 2/2... Steps 7250 Discriminator Loss: 0.9300... Generator Loss: 1.3244
Epoch 2/2... Steps 7300 Discriminator Loss: 0.9176... Generator Loss: 1.5006
Epoch 2/2... Steps 7350 Discriminator Loss: 1.0285... Generator Loss: 0.9702
Epoch 2/2... Steps 7400 Discriminator Loss: 1.3973... Generator Loss: 0.5340
Epoch 2/2... Steps 7450 Discriminator Loss: 1.2353... Generator Loss: 0.7374
Epoch 2/2... Steps 7500 Discriminator Loss: 1.0068... Generator Loss: 0.8548
Epoch 2/2... Steps 7550 Discriminator Loss: 1.0218... Generator Loss: 2.2637
Epoch 2/2... Steps 7600 Discriminator Loss: 1.2683... Generator Loss: 1.4675
Epoch 2/2... Steps 7650 Discriminator Loss: 1.3686... Generator Loss: 0.4832
Epoch 2/2... Steps 7700 Discriminator Loss: 1.3507... Generator Loss: 0.8420
Epoch 2/2... Steps 7750 Discriminator Loss: 1.0960... Generator Loss: 0.7437
Epoch 2/2... Steps 7800 Discriminator Loss: 1.4186... Generator Loss: 0.5527
Epoch 2/2... Steps 7850 Discriminator Loss: 1.1938... Generator Loss: 0.6766
Epoch 2/2... Steps 7900 Discriminator Loss: 1.2793... Generator Loss: 0.7151
Epoch 2/2... Steps 7950 Discriminator Loss: 0.9391... Generator Loss: 0.9517
Epoch 2/2... Steps 8000 Discriminator Loss: 1.7305... Generator Loss: 0.3292
Epoch 2/2... Steps 8050 Discriminator Loss: 0.8337... Generator Loss: 1.4174
Epoch 2/2... Steps 8100 Discriminator Loss: 0.9979... Generator Loss: 0.8909
Epoch 2/2... Steps 8150 Discriminator Loss: 0.9196... Generator Loss: 1.1563
Epoch 2/2... Steps 8200 Discriminator Loss: 0.9352... Generator Loss: 1.7286
Epoch 2/2... Steps 8250 Discriminator Loss: 0.9958... Generator Loss: 1.4554
Epoch 2/2... Steps 8300 Discriminator Loss: 1.5950... Generator Loss: 0.4301
Epoch 2/2... Steps 8350 Discriminator Loss: 0.5975... Generator Loss: 2.6694
Epoch 2/2... Steps 8400 Discriminator Loss: 0.9680... Generator Loss: 1.1778
Epoch 2/2... Steps 8450 Discriminator Loss: 1.0435... Generator Loss: 1.1917
Epoch 2/2... Steps 8500 Discriminator Loss: 1.3839... Generator Loss: 0.5430
Epoch 2/2... Steps 8550 Discriminator Loss: 0.8847... Generator Loss: 1.4619
Epoch 2/2... Steps 8600 Discriminator Loss: 1.1725... Generator Loss: 0.6970
Epoch 2/2... Steps 8650 Discriminator Loss: 1.2355... Generator Loss: 0.6022
Epoch 2/2... Steps 8700 Discriminator Loss: 1.0143... Generator Loss: 0.9009
Epoch 2/2... Steps 8750 Discriminator Loss: 1.0312... Generator Loss: 1.6331
Epoch 2/2... Steps 8800 Discriminator Loss: 1.3516... Generator Loss: 0.5453
Epoch 2/2... Steps 8850 Discriminator Loss: 1.2685... Generator Loss: 0.5418
Epoch 2/2... Steps 8900 Discriminator Loss: 1.2185... Generator Loss: 0.7641
Epoch 2/2... Steps 8950 Discriminator Loss: 1.0527... Generator Loss: 1.4806
Epoch 2/2... Steps 9000 Discriminator Loss: 1.0105... Generator Loss: 0.9598
Epoch 2/2... Steps 9050 Discriminator Loss: 1.0652... Generator Loss: 0.7125
Epoch 2/2... Steps 9100 Discriminator Loss: 0.9494... Generator Loss: 0.9133
Epoch 2/2... Steps 9150 Discriminator Loss: 1.0441... Generator Loss: 0.8388
Epoch 2/2... Steps 9200 Discriminator Loss: 1.0100... Generator Loss: 0.8253
Epoch 2/2... Steps 9250 Discriminator Loss: 1.3365... Generator Loss: 0.5193
Epoch 2/2... Steps 9300 Discriminator Loss: 1.2796... Generator Loss: 0.7878
Epoch 2/2... Steps 9350 Discriminator Loss: 1.1523... Generator Loss: 0.7646
Epoch 2/2... Steps 9400 Discriminator Loss: 1.3398... Generator Loss: 0.5187
Epoch 2/2... Steps 9450 Discriminator Loss: 1.1542... Generator Loss: 0.8349
Epoch 2/2... Steps 9500 Discriminator Loss: 1.1664... Generator Loss: 0.7835
Epoch 2/2... Steps 9550 Discriminator Loss: 1.4333... Generator Loss: 0.4662
Epoch 2/2... Steps 9600 Discriminator Loss: 1.1982... Generator Loss: 0.8662
Epoch 2/2... Steps 9650 Discriminator Loss: 0.9064... Generator Loss: 1.4647
Epoch 2/2... Steps 9700 Discriminator Loss: 1.0043... Generator Loss: 1.4149
Epoch 2/2... Steps 9750 Discriminator Loss: 1.8484... Generator Loss: 1.9790
Epoch 2/2... Steps 9800 Discriminator Loss: 1.2291... Generator Loss: 1.6038
Epoch 2/2... Steps 9850 Discriminator Loss: 1.1016... Generator Loss: 1.3825
Epoch 2/2... Steps 9900 Discriminator Loss: 1.2817... Generator Loss: 0.6843
Epoch 2/2... Steps 9950 Discriminator Loss: 1.1470... Generator Loss: 0.8203
Epoch 2/2... Steps 10000 Discriminator Loss: 1.3347... Generator Loss: 0.5302
Epoch 2/2... Steps 10050 Discriminator Loss: 1.2275... Generator Loss: 0.6026
Epoch 2/2... Steps 10100 Discriminator Loss: 1.1535... Generator Loss: 0.6800
Epoch 2/2... Steps 10150 Discriminator Loss: 1.3115... Generator Loss: 0.6304
Epoch 2/2... Steps 10200 Discriminator Loss: 1.4115... Generator Loss: 0.4983
Epoch 2/2... Steps 10250 Discriminator Loss: 1.4901... Generator Loss: 0.4319
Epoch 2/2... Steps 10300 Discriminator Loss: 1.1561... Generator Loss: 0.7245
Epoch 2/2... Steps 10350 Discriminator Loss: 1.3543... Generator Loss: 0.5099
Epoch 2/2... Steps 10400 Discriminator Loss: 1.4073... Generator Loss: 0.8180
Epoch 2/2... Steps 10450 Discriminator Loss: 1.1512... Generator Loss: 0.7815
Epoch 2/2... Steps 10500 Discriminator Loss: 1.3500... Generator Loss: 0.5097
Epoch 2/2... Steps 10550 Discriminator Loss: 1.0093... Generator Loss: 1.0496
Epoch 2/2... Steps 10600 Discriminator Loss: 0.9641... Generator Loss: 0.9550
Epoch 2/2... Steps 10650 Discriminator Loss: 1.0354... Generator Loss: 0.9154
Epoch 2/2... Steps 10700 Discriminator Loss: 1.3476... Generator Loss: 0.5644
Epoch 2/2... Steps 10750 Discriminator Loss: 1.0244... Generator Loss: 0.8960
Epoch 2/2... Steps 10800 Discriminator Loss: 0.9893... Generator Loss: 1.3158
Epoch 2/2... Steps 10850 Discriminator Loss: 1.0688... Generator Loss: 0.9827
Epoch 2/2... Steps 10900 Discriminator Loss: 1.1576... Generator Loss: 1.0652
Epoch 2/2... Steps 10950 Discriminator Loss: 0.9454... Generator Loss: 1.2148
Epoch 2/2... Steps 11000 Discriminator Loss: 1.1985... Generator Loss: 0.6958
Epoch 2/2... Steps 11050 Discriminator Loss: 0.8472... Generator Loss: 1.2961
Epoch 2/2... Steps 11100 Discriminator Loss: 1.0196... Generator Loss: 0.9688
Epoch 2/2... Steps 11150 Discriminator Loss: 1.3045... Generator Loss: 1.0452
Epoch 2/2... Steps 11200 Discriminator Loss: 1.1017... Generator Loss: 1.4894
Epoch 2/2... Steps 11250 Discriminator Loss: 0.6893... Generator Loss: 1.4277
Epoch 2/2... Steps 11300 Discriminator Loss: 1.2207... Generator Loss: 0.6653
Epoch 2/2... Steps 11350 Discriminator Loss: 0.8920... Generator Loss: 1.0769
Epoch 2/2... Steps 11400 Discriminator Loss: 1.2530... Generator Loss: 0.5915
Epoch 2/2... Steps 11450 Discriminator Loss: 1.3418... Generator Loss: 0.7587
Epoch 2/2... Steps 11500 Discriminator Loss: 0.7438... Generator Loss: 1.7175
Epoch 2/2... Steps 11550 Discriminator Loss: 1.1395... Generator Loss: 0.7134
Epoch 2/2... Steps 11600 Discriminator Loss: 1.0090... Generator Loss: 0.8742
Epoch 2/2... Steps 11650 Discriminator Loss: 0.9093... Generator Loss: 1.1842
Epoch 2/2... Steps 11700 Discriminator Loss: 1.2438... Generator Loss: 1.4529
Epoch 2/2... Steps 11750 Discriminator Loss: 0.9911... Generator Loss: 1.6497
Epoch 2/2... Steps 11800 Discriminator Loss: 1.0051... Generator Loss: 1.6134
Epoch 2/2... Steps 11850 Discriminator Loss: 0.8382... Generator Loss: 1.2795
Epoch 2/2... Steps 11900 Discriminator Loss: 0.5748... Generator Loss: 1.9404
Epoch 2/2... Steps 11950 Discriminator Loss: 1.5301... Generator Loss: 0.4088
Epoch 2/2... Steps 12000 Discriminator Loss: 0.7422... Generator Loss: 1.4041
Epoch 2/2... Steps 12050 Discriminator Loss: 0.9317... Generator Loss: 1.0977
Epoch 2/2... Steps 12100 Discriminator Loss: 1.0985... Generator Loss: 0.7774
Epoch 2/2... Steps 12150 Discriminator Loss: 1.1953... Generator Loss: 0.6430
Epoch 2/2... Steps 12200 Discriminator Loss: 0.9931... Generator Loss: 1.0860
Epoch 2/2... Steps 12250 Discriminator Loss: 1.3988... Generator Loss: 0.4980
Epoch 2/2... Steps 12300 Discriminator Loss: 2.5569... Generator Loss: 0.1495
Epoch 2/2... Steps 12350 Discriminator Loss: 1.5748... Generator Loss: 0.3910
Epoch 2/2... Steps 12400 Discriminator Loss: 0.9260... Generator Loss: 1.0209
Epoch 2/2... Steps 12450 Discriminator Loss: 1.1038... Generator Loss: 1.6179
Epoch 2/2... Steps 12500 Discriminator Loss: 0.8789... Generator Loss: 1.2100
Epoch 2/2... Steps 12550 Discriminator Loss: 0.8540... Generator Loss: 1.4810
Epoch 2/2... Steps 12600 Discriminator Loss: 1.1449... Generator Loss: 1.9071
Epoch 2/2... Steps 12650 Discriminator Loss: 0.8679... Generator Loss: 1.1569

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.